Skip to main content

Using the Raspberry PI2 Module and the Brain-Computer Technology for Controlling a Mobile Vehicle

  • Conference paper
  • First Online:
Automation 2019 (AUTOMATION 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 920))

Included in the following conference series:

Abstract

This paper describes the execution process of a four-wheeled robot controlled by a user via an Emotiv EPOC+ NeuroHeadset device. The following, inter alia, was described for this purpose - the issue of selecting a controller with additional modules necessary to create a robot; execution of a four-wheeler prototype; connecting the devices: Raspberry PI2 and Emotiv EPOC+ NeuroHeadset in a network, which allows the transfer of data grouped in packs. An original control algorithm, presented in this paper was developed and calibration with an Emotiv EPOC+ NeuroHeadset device was conducted for the purposes of the research.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Badcock, N.A., et al.: Validation of the Emotiv EPOC EEG system for research quality auditory event-related potentials in children. Peer J. 3, 907 (2015). http://dx.doi.org/10.7717/peerj.907

  2. Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134(1), 9–21 (2004). https://doi.org/10.1016/j.jneumeth.2003.10.009

    Article  Google Scholar 

  3. Mathewson, K.E., Lleras, A., Beck, D.M., Fabiani, M., Ro, T., Gratton, G.: Pulsed out of awareness: EEG alpha oscillations represent a pulsed-inhibition of ongoing cortical processing. Front. Psychol. (2011). https://doi.org/10.3389/fpsyg.2011.00099

  4. Ghaemi, A., Rashedi, E., Pourrahimi, A.M., Kamandar, M., Rahdari, F.: Automatic channel selection in EEG signals for classification of left or right hand movement in BCI using improved binary gravitation search algorithm. Biomed. Sign. Process. Control 33, 109–118 (2017). https://doi.org/10.1016/j.bspc.2016.11.018

    Article  Google Scholar 

  5. Lin, Y., Breugelmans, J., Iversen, M., Schmidt, D.: An adaptive interface design (AID) for enhanced computer accessibility and rehabilitation. Int. J. Hum. Comput. Stud. 98, 14–23 (2017). https://doi.org/10.1016/j.ijhcs.2016.09.012

    Article  Google Scholar 

  6. Gareis, I.E., Vignolo, L.D., Spies, R.D., Rufiner, H.L.: Coherent averaging estimation autoencoders applied to evoked potentials processing. Neurocomputing 240(31), 47–58 (2017). https://doi.org/10.1016/j.neucom.2017.02.050

    Article  Google Scholar 

  7. Kuziek, J.W.P., Shienh, A., Mathewson, K.E.: Transitioning EEG experiments away from the laboratory using a Raspberry Pi 2. J. Neurosci. Methods 277(1), 75–82 (2017). https://doi.org/10.1016/j.jneumeth.2016.11.013

    Article  Google Scholar 

  8. Bolaños, F., LeDue, J.M., Murphy, T.H.: Cost effective raspberry pi-based radio frequency identification tagging of mice suitable for automated in vivo imaging. J. Neurosci. Methods 276(30), 79–83 (2017). https://doi.org/10.1016/j.jneumeth.2016.11.011

    Article  Google Scholar 

  9. Arcidiacono, C., Porto, S.M.C., Mancino, M., Cascone, G.: Development of a threshold-based classifier for real-time recognition of cow feeding and standing behavioural activities from accelerometer data. Comput. Electron. Agric. 134, 124–134 (2017). https://doi.org/10.1016/j.compag.2017.01.021

    Article  Google Scholar 

  10. Paszkiel, S.: Characteristics of question of blind source separation using Moore-Penrose pseudo inversion for reconstruction of EEG signal. In: Szewczyk, R., Zieliski, C., Kaliczyska, M. (eds.) Recent Research in Automation, Robotics and Measuring Techniques. Series: Challenges in Automation, Robotics and Measurement Techniques, Advances in Intelligent Systems and Computing. Springer, Cham (2017)

    Chapter  Google Scholar 

  11. Paszkiel, S., Hunek, W., Shylenko, A.: Project and simulation of a portable proprietary device for measuring bioelectrical signals from the brain for verification states of consciousness with visualization on LEDs. In: Szewczyk, R., Zieliski, C., Kaliczyska, M. (eds.) Recent Research in Automation, Robotics and Measuring Techniques. Series: Challenges in Automation, Robotics and Measurement Techniques, Advances in Intelligent Systems and Computing, vol. 440, pp. 25–36. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-29357-8

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Szczepan Paszkiel .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Paszkiel, S. (2020). Using the Raspberry PI2 Module and the Brain-Computer Technology for Controlling a Mobile Vehicle. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds) Automation 2019. AUTOMATION 2019. Advances in Intelligent Systems and Computing, vol 920. Springer, Cham. https://doi.org/10.1007/978-3-030-13273-6_34

Download citation

Publish with us

Policies and ethics